Building AI agents with LangChain
LangChain agents are built on LangGraph : the model calls tools in a loop until it returns a final answer. The high-level entry point is createAgent -…
Testing & QA
⚑ Report a ProblemLatest Testing & QA news from Tech News
All topics
agents
ai
api
architecture
automation
aws
beginners
career
claude
cybersecurity
database
devchallenge
devops
discuss
javascript
llm
machinelearning
mcp
opensource
performance
productivity
programming
python
security
showdev
softwareengineering
testing
tutorial
typescript
webdev
Agent Series (20): Harness in Production — From Single File to Reusable Package
From Demo Code to a Reusable Package Article 19 used a 900-line harness_full_demo.py to demonstrate eight defense layers. That file is good for explai…
LangChain + Chroma: Multi-turn RAG Memory and Automated Testing That Turned 2-Hour Bugs Into 5-Minute Fixes
At 1 a.m., the customer group chat exploded: “Does your customer service bot have only a 7-second memory? I just gave it the order number, and the nex…
How to Build an AI Resume Builder with LangChain and Node.js
A few months back, my friend Marcus was applying for a senior backend role at a fintech company. He had five years of solid experience — distributed s…
Agent Series (10): MCP Protocol — Standardizing the Tool Ecosystem
More Tools, More Chaos After you build an Agent, the first thing you usually do is give it tools: search, code execution, database queries, API calls……
How to Add Memory Security to Your LangChain Agent in 5 Minutes
Why Your Agent's Memory Needs Security If you're building LangChain agents with persistent memory (ConversationBufferMemory, RedisChatMessageHistory, …
Agent Series (3): Plan-and-Solve — Think First, Then Act
Where Does ReAct Hit a Wall? The previous article established ReAct's greedy strategy — each step looks at only the current state and decides the next…
Three Design Decisions That Shaped the Enterprise RAG Retrieval Pipeline
Enterprise RAG — A practitioner's build log | Post 3 of 6 A retrieval pipeline has more design surface than it appears. The technology choices — vecto…
AgentKit vs LangChain vs Direct HTTP — picking the right integration for paid agent APIs
When you're plugging an LLM agent into an external API, you have three reasonable patterns: hand-rolled HTTP, AgentKit's action provider model, or Lan…
LangChain ChromaDB Metadata Priority Injection — RAG Poisoning Vulnerability
LangChain ChromaDB Metadata Priority Injection Vulnerability Summary LangChain's Chroma integration allows attackers to manipulate document retrieval …
AI Agent Frameworks Compared: LangChain vs Custom vs Agentic Systems
AI agents are rapidly moving from experimental prototypes to production infrastructure. Developers are no longer just building chatbots. They’re build…
Deep Dive: LangChain 0.3 LCEL and How It Optimizes Claude 3.7 Calls
If you’ve ever watched a Claude 3.7 API call sit in a pending state for 2.1 seconds while your LangChain pipeline serializes prompts, parses outputs, …
Madrigal's "Failures as Eval Suites" Pattern and How Flow Already Provides the Infrastructure
A blog post on LangChain's site about how Madrigal Pharmaceuticals built their multi-agent AI platform caught my attention this week. Not because of t…
How to Monetize Your AI Agents with LangChain and Kong
Say you built an AI agent and customers are starting to pay for it. Sooner or later you'll want to charge them by what they actually use, because some…
Build a RAG agent with LangChain and Ollama
I started where a lot of us do: a LangChain RAG walkthrough. You chunk some text, embed it, retrieve top‑k chunks, and wire an LLM to answer questions…